Google's Making Its Own Chips Now. Time for Intel to Freak Out

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Google's Making Its Own Chips Now. Time for Intel to Freak Out

Urs Hölzle.

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Google has built its own computer chip. And this won't be the last.

The Internet's most powerful company sent a few shock waves through the tech world yesterday when it revealed that a new custom-designed chip helps run what is surely the future of its vast online empire: artificial intelligence.

The effect of Google's chip extends well beyond the Google empire. It threatens the future of the chip industry itself.

With its new chip, Google's aim is the same: unprecedented efficiency. To take AI to new heights, it needs a chip that can do more in less time while consuming less power. But the effect of this chip extends well beyond the Google empire. It threatens the future of commercial chip makers like Intel and nVidia—particularly when you consider Google's vision for the future. According to Urs Hölzle, the man most responsible for the global data center network that underpins the Google empire, this new custom chip is just the first of many.

No, Google will not sell its chips to other companies. It won't directly compete with Intel or nVidia. But with its massive data centers, Google is by far the largest potential customer for both of those companies. At the same time, as more and more businesses adopt the cloud computing services offered by Google, they'll be buying fewer and fewer servers (and thus chips) of their own, eating even further into the chip market.

Indeed, Google revealed its new chip as a way of promoting the cloud services that let businesses and coders tap into its AI engines and build them into their own applications. As Google tries to sell other companies on the power of its AI, it's claiming—in rather loud ways—that it boasts the best hardware for running this AI, hardware that no other company has.

According to Google, it tailored the TPU specifically to machine learning so that it needs fewer transistors to run each operation. That means it can squeeze more operations into the chip with each passing second.

For now, Google is using both TPUs and GPUs to run its neural nets. Hölzle declined to go into specifics on how exactly Google was using its TPUs, except to say that they handle "part of the computation" needed to drive voice recognition on Android phones. But he said that Google would be releasing a paper describing the benefits of its chip and that Google will continue to design new chips that handle machine learning in other ways. Eventually, it seems, this will push GPUs out of the equation. "They're already going away a little," Hölzle says. "The GPU is too general for machine learning. It wasn't actually built for that."

That's not something nVidia wants to hear. As the world's primary seller of GPUs, nVidia is now pushing to expand its own business into the AI realm. As Hölzle points out, the latest nVidia GPU offers a mode specifically for machine learning. But clearly, Google wants the change to happen faster. Much faster.

The Smartest Chip

In the meantime, other companies, most notably Microsoft, are exploring another breed of chip. The field-programmable gate array, or FPGA, is a chip you can re-program to perform specific tasks. Microsoft has tested FPGAs with machine learning, and Intel, seeing where this market was going, recently acquired a company that sells FPGAs.

Some analysts think that's the smarter way to go. An FPGA provides far more flexibility, says Patrick Moorhead, the president and principal analyst at Moor Insights and Strategy, a firm that closely follows the chip business. Moorhead wonders if the new Google TPU is "overkill," pointing out that such a chip takes at least six months to build—a long time in the incredibly competitive marketplace in which the biggest Internet companies compete.

But Google doesn't want that flexibility. More than anything, it wants speed. Asked why Google built its chip from scratch rather than using an FPGA, Hölzle said: "It's just much faster."

Core Business

Hölzle also points out that Google's chip doesn't replace CPUs, the central processing units at the heart of every computer server. The search giant still needs these chips to run the tens of thousands of machines in its data centers, and CPUs are Intel's main business. Still, if Google is willing to build its own chips just for AI, you have to wonder if it would go so far as to design its own CPUs as well.

Hölzle plays down the possibility. "You want to solve problems that are not solved," he says. In other words, CPUs are a mature technology that pretty much works as it should. But he also said that Google wants healthy competition in the chip market. In other words, it wants to buy from many sellers—not just, say, Intel. After all, more competition means lower prices for Google. As Hölzle explains, expanding its options is why Google is working with the OpenPower Foundation, which seeks to offer chip designs that anyone can use and modify.

That's a powerful idea, and a potentially powerful threat to the world's biggest chip makers. According to Shane Rau, an analyst with research firm IDC, Google buys about 5 percent of all server CPUs sold on Earth. Over a recent year-long period, he says, Google bought about 1.2 million chips. And most of those likely came from Intel. (In 2012, Intel exec Diane Bryant told WIRED that Google bought more server chips from Intel than all but five other companies—and those were all companies that sell servers.)

Whatever its plans for the CPU, Google will continue to explore chips specifically suited to machine learning. It will be several years before we really know what works and what doesn't. After all, neural networks are constantly evolving as well. "We're learning all the time," he says. "It's not clear to me what the final answer is." And as it learns, you can bet that the world's chip makers will be watching.

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